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This case study delves into the evolving landscape of equity, inclusion and diversity (EID) initiatives within the health-care sector, with a specific focus on the “EverybodyIN” program implemented at the Mayo Clinic, a large academic Medical Center in the USA. Against the backdrop of growing awareness catalyzed by societal events, this case study aims to explore the multifaceted aspects of workplace conversations aimed at addressing racial disparities and fostering a more inclusive environment.

The case study relies on the application of critical race theory and a social constructionist approach to investigate the impact of a subset of voluntary educational conversations that were centered on the Black/African-American experience, on staff members’ racial understanding and allyship within the health-care organization. Through thematic analysis of postevent surveys and participant sentiments, three overarching themes emerged: appreciation, education and validation.

Through thematic analysis of postevent surveys and participant sentiments, three overarching themes emerged: appreciation, education and validation. The findings underscore the pivotal role of leadership buy-in, evidence-based practices, health equity and an ongoing commitment to “the journey” in successful EID efforts. The results highlight the significance of integrating EID into health-care organizations as a continuous endeavor that aligns with organizational values and mission.

The findings underscore the pivotal role that theory and practice play through a newly described framework that includes leadership buy-in, evidence-based practices, health equity and an ongoing commitment to “the journey” for successful EID efforts.

The results highlight the significance of integrating EID into health-care organizations as a continuous endeavor that aligns with organizational values and mission.

By fostering a safe and informed space for dialogue, organizations can empower staff to engage authentically and acquire cultural competence that may contribute to advancing health equity.

The increased digital interactions in the B2B industry have enhanced the importance of customer engagement as a measure of firm performance. This study aims to map and analyze temporal and spatial journeys for customer engagement in B2B markets from a bibliometric perspective.

The extant literature on customer engagement research in the B2B context was analyzed using bibliometric analysis. The citation analysis, keyword analysis, cluster analysis, three-field plot and bibliographic coupling were used to map the intellectual structure of customer engagement in B2B markets.

The research on customer engagement in the B2B context was studied more in western countries. The analysis suggests that customer engagement in B2B markets will take centre stage in the coming times as digital channels make it easier to track critical metrics besides other key factors. Issues like digital transformation, the use of artificial intelligence for virtual engagement, personalization, innovation and salesforce management by leveraging technology would be critical for improved B2B customer engagement.

The study provides a comprehensive reference to scholars working in this domain.

The study makes a pioneering effort to comprehensively analyze the vast corpus of literature on customer engagement in B2B markets for business insights.

This study aims to develop a methodology for predicting the resilience of individual firms to economic crisis, using historical government data to optimize one of the most important and costly interventions that governments undertake, the huge economic stimulus programs that governments implement for mitigating the consequences of economic crises, by making them more focused on the less resilient and more vulnerable firms to the crisis, which have the highest need for government assistance and support.

The authors are leveraging existing firm-level data for economic crisis periods from government agencies having competencies/responsibilities in the domain of economy, such as Ministries of Finance and Statistical Authorities, to construct prediction models of the resilience of individual firms to the economic crisis based on firms’ characteristics (such as human resources, technology, strategies, processes and structure), using artificial intelligence (AI) techniques from the area of machine learning (ML).

The methodology has been applied using data from the Greek Ministry of Finance and Statistical Authority about 363 firms for the Greek economic crisis period 2009–2014 and has provided a satisfactory prediction of a measure of the resilience of individual firms to an economic crisis.

The authors’ study opens up new research directions concerning the exploitation of AI/ML in government for a critical government activity/intervention of high importance that mobilizes/spends huge financial resources. The main limitation is that the abovementioned first application of the proposed methodology has been based on a rather small data set from a single national context (Greece), so it is necessary to proceed to further application of this methodology using larger data sets and different national contexts.

The proposed methodology enables government agencies responsible for the implementation of such economic stimulus programs to proceed to radical transformations of them by predicting the resilience to economic crisis of the firms applying for government assistance and then directing/focusing the scarce available financial resources to/on the ones predicted to be more vulnerable, increasing substantially the effectiveness of these programs and the economic/social value they generate.

To the best of the authors’ knowledge, this study is the first application of AI/ML in government that leverages existing data for economic crisis periods to optimize and increase the effectiveness of the largest and most important and costly economic intervention that governments repeatedly have to make: the economic stimulus programs for mitigating the consequences of economic crises.